10 research outputs found

    Innovative Techniques for the Retrieval of Earth’s Surface and Atmosphere Geophysical Parameters: Spaceborne Infrared/Microwave Combined Analyses

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    With the advent of the first satellites for Earth Observation: Landsat-1 in July 1972 and ERS-1 in May 1991, the discipline of environmental remote sensing has become, over time, increasingly fundamental for the study of phenomena characterizing the planet Earth. The goal of environmental remote sensing is to perform detailed analyses and to monitor the temporal evolution of different physical phenomena, exploiting the mechanisms of interaction between the objects that are present in an observed scene and the electromagnetic radiation detected by sensors, placed at a distance from the scene, operating at different frequencies. The analyzed physical phenomena are those related to climate change, weather forecasts, global ocean circulation, greenhouse gas profiling, earthquakes, volcanic eruptions, soil subsidence, and the effects of rapid urbanization processes. Generally, remote sensing sensors are of two primary types: active and passive. Active sensors use their own source of electromagnetic radiation to illuminate and analyze an area of interest. An active sensor emits radiation in the direction of the area to be investigated and then detects and measures the radiation that is backscattered from the objects contained in that area. Passive sensors, on the other hand, detect natural electromagnetic radiation (e.g., from the Sun in the visible band and the Earth in the infrared and microwave bands) emitted or reflected by the object contained in the observed scene. The scientific community has dedicated many resources to developing techniques to estimate, study and analyze Earth’s geophysical parameters. These techniques differ for active and passive sensors because they depend strictly on the type of the measured physical quantity. In my P.h.D. work, inversion techniques for estimating Earth’s surface and atmosphere geophysical parameters will be addressed, emphasizing methods based on machine learning (ML). In particular, the study of cloud microphysics and the characterization of Earth’s surface changes phenomenon are the critical points of this work

    Implementation of a Discrete Dipole Approximation Scattering Database Into Community Radiative Transfer Model

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    The Community Radiative Transfer Model (CRTM) is a fast model that requires bulk optical properties of hydrometeors in the form of lookup tables to simulate all-sky satellite radiances. Current cloud scattering lookup tables of CRTM were generated using the Mie-Lorenz theory thus assuming spherical shapes for all frozen habits, while actual clouds contain frozen hydrometeors with different shapes. The Discrete Dipole Approximation (DDA) technique is an effective technique for simulating the optical properties of non-spherical hydrometeors in the microwave region. This paper discusses the implementation and validation of a comprehensive DDA cloud scattering database into CRTM for the microwave frequencies. The original DDA database assumes total random orientation in the calculation of single scattering properties. The mass scattering parameters required by CRTM were then computed from single scattering properties and water content dependent particle size distributions. The new lookup tables eliminate the requirement for providing the effective radius as input to CRTM by using the cloud water content for the mass dimension. A collocated dataset of short-term forecasts from Integrated Forecast System of the European Center for Medium-Range Weather Forecasts and satellite microwave data was used for the evaluation of results. The results overall showed that the DDA lookup tables, in comparison with the Mie tables, greatly reduce the differences among simulated and observed values. The Mie lookup tables especially introduce excessive scattering for the channels operating below 90\ua0GHz and low scattering for the channels above 90\ua0GHz

    Challenges in measuring winter precipitation : Advances in combining microwave remote sensing and surface observations

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    Globally, snow influences Earth and its ecosystems in several ways by having a significant impact on, e.g., climate and weather, Earth radiation balance, hydrology, and societal infrastructures. In mountainous regions and at high latitudes snowfall is vital in providing freshwater resources by accumulating water within the snowpack and releasing the water during the warm summer season. Snowfall also has an impact on transportation services, both in aviation and road maintenance. Remote sensing instrumentation, such as radars and radiometers, provide the needed temporal and spatial coverage for monitoring precipitation globally and on regional scales. In microwave remote sensing, the quantitative precipitation estimation is based on the assumed relations between the electromagnetic and physical properties of hydrometeors. To determine these relations for solid winter precipitation is challenging. Snow particles have an irregular structure, and their properties evolve continuously due to microphysical processes that take place aloft. Hence also the scattering properties, which are dependent on the size, shape, and dielectric permittivity of the hydrometeors, are changing. In this thesis, the microphysical properties of snowfall are studied with ground-based measurements, and the changes in prevailing snow particle characteristics are linked to remote sensing observations. Detailed ground observations from heavily rimed snow particles to openstructured low-density snowflakes are shown to be connected to collocated triple-frequency signatures. As a part of this work, two methods are implemented to retrieve mass estimates for an ensemble of snow particles combining observations of a video-disdrometer and a precipitation gauge. The changes in the retrieved mass-dimensional relations are shown to correspond to microphysical growth processes. The dependence of the C-band weather radar observations on the microphysical properties of snow is investigated and parametrized. The results apply to improve the accuracy of the radar-based snowfall estimation, and the developed methodology also provides uncertainties of the estimates. Furthermore, the created data set is utilized to validate space-borne snowfall measurements. This work demonstrates that the C-band weather radar signal propagating through a low melting layer can significantly be attenuated by the melting snow particles. The expected modeled attenuation is parametrized according to microphysical properties of snow at the top of the melting layer

    Co-located analysis of ice clouds detected from space and their impact on longwave energy transfer

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    A lack of quality data on high clouds has led to inadequate representations within global weather and climate models. Recent advances in spaceborne measurements of the Earth’s atmosphere have provided complementary information on the interior of these clouds. This study demonstrate how an array of space-borne measurements can be used and combined, by close co-located comparisons in space and time, to form a more complete representation of high cloud processes and properties. High clouds are found in the upper atmosphere, where sub-zero temperatures frequently result in the formation of cloud particles that are composed of ice. Weather and climate models characterise the bulk properties of these ice particles to describe the current state of the cloud-sky atmosphere. By directly comparing measurements with simulations undertaken at the same place and time, this study demonstrates how improvements can be made to the representation of cloud properties. The results from this study will assist in the design of future cloud missions to provide a better quality input. These improvements will also help improve weather predictions and lower the uncertainty in cloud feedback response to increasing atmospheric temperature. Most clouds are difficult to monitor by more than one instrument due to continuous changes in: large-scale and sub-cloud scale circulation features, microphysical properties and processes and characteristic chemical signatures. This study undertakes co-located comparisons of high cloud data with a cloud ice dataset reported from the Microwave Limb Sounder (MLS) instrument onboard the Aura satellite that forms part of the A-train constellation. Data from the MLS science team include vertical profiles of temperature, ice water content (IWC) and the mixing ratios of several trace gases. Their vertical resolutions are 3 to 6 km. Initial investigations explore the link between cloud-top properties and the longwave radiation budget, developing methods for estimating cloud top heights using; longwave radiative fluxes, and IWC profiles. Synergistic trios of direct and indirect high cloud measurements were used to validate detections from the MLS by direct comparisons with two different A-train instruments; the NASA Moderate-resolution Imaging Spectroradiometer (MODIS) and the Clouds and the Earth’s Radiant Energy System (CERES) onboard on the Aqua satellite. This finding focuses later studies on two high cloud scene types that are well detected by the MLS; deep convective plumes that form from moist ascent, and their adjacent outflows that emanate outwards several hundred kilometres. The second part of the thesis identifies and characterises two different high cloud scenes in the tropics. Direct observational data is used to refine calculations of the climate sensitivity to upper tropospheric humidity and high cloud in different conditions. The data reveals several discernible features of convective outflows are identified using a large sample of MLS data. The key finding, facilitated by the use of co-location, reveals that deep convective plumes exert a large longwave warming effect on the local climate of 52 ± 28Wm−2, with their adjacent outflows presenting a more modest warming of 33 ± 20Wm−2

    Characterization of snowfall using ground-based passive and active remote sensors.

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    Snowfall is a key quantity in the global hydrological cycle and has an impact on the global energy budget as well. In sub-polar and polar latitudes, snowfall is the predominant type of precipitation and rainfall is often initiated via the ice phase. Currently, the spatial distribution of snowfall is poorly captured by numerical weather prediction and climate models. In order to evaluate the models and to improve our understanding of snowfall microphysics, global observations of snowfall are needed. This can only be obtained by space-borne active and passive remote sensors. In order to be able to penetrate even thick snow clouds, sensors operating in the microwave frequency region are favoured. The challenge for snowfall retrieval development lies first in the complexity of snowfall microphysics and its interactions with liquid cloud water. Secondly, comprehensive knowledge is needed about the interaction of electromagnetic radiation with snowfall in order to finally relate the radiative signatures to physical quantities. A general advantage of ground-based observations is that simultaneous measurements of in-situ and remote sensing instruments can be obtained. Such a six-month dataset was collected within this thesis at an alpine site. The instrumentation included passive microwave radiometers that covered the frequency range from 22 up to \unit[150]{GHz} as well as two radar systems operating at 24.1 and 35.5 GHz. These data were complemented by optical disdrometer, ceilometer and various standard meteorological measurements. State-of-the-art single scattering databases for pristine ice crystals and complex snow aggregates were used within this thesis to investigate the sensitivity of ground--based passive and active remote sensors to various snowfall parameters such as vertical snow and liquid water distribution, snow particle habit, snow size distribution and ground surface properties. The comparison of simulations with measurements within a distinct case study revealed that snow particle scattering can be measured with ground--based passive microwave sensors at frequencies higher than 90 GHz. Sensitivity experiments further revealed that ground-based sensors have clear advantages over nadir measuring instruments due to a stronger snow scattering signal and lower sensitivity to variable ground surface emissivity. However, passive sensors were also found to be highly sensitive to liquid cloud water that was frequently observed during the entire campaign. The simulations indicate that the uncertainties of sizes distribution and snow particle habit are not distinguishable with a passive-only approach. In addition to passive microwave observations, data from a low-end radar system that is commonly used for rainfall were investigated for its capabilities to observe snowfall. For this, a snowfall specific data processing algorithm was developed and the re-processed data were compared to collocated measurements of a high-end cloud radar. If the focus can be narrowed down to medium and strong snowfall within the lowest 2-3 km height, the reflectivity and fall velocity measurements of the low-end system agree well with the cloud radar. The cloud radar dataset was used to estimate the uncertainty of retrieved snowfall rate and snow accumulation of the low-end system. Besides the intrinsic uncertainties of single-frequency radar retrievals the estimates of total snow accumulation by the low-end system lay within 7% compared to the cloud radar estimates. In a more general approach, the potential of multi-frequency radar systems for derivation of snow size distribution parameters and particle habit were investigated within a theoretical simulation study. Various single-scattering databases were combined to test the validity of dual-frequency approaches when applied to non-spheroid particle habits. It was found that the dual-frequency technique is dependent on particle habit. It could be shown that a rough distinction of snow particle habits can be achieved by a combination of three frequencies. The method was additionally tested with respect to signal attenuation and maximum particle size. The results obtained by observations and simulations within this thesis strongly suggest the further development of simultaneous ground-based in-situ and remote sensing observations of snowfall. Extending the sensitivity studies of this study will help to define the most suitable set of sensors for future studies. A combination of these measurements with a further development of single-scattering databases will potentially help to improve our understanding of snowfall microphysics

    Potential of millimeter- and submillimeter-wave satellite observations for hydrometeor studies

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    The distribution of hydrometeors is highly variable in space and time, since it is the result of a complex chain of processes with scales from microphysical (1e-6 m) to synoptical (1e3 m). It is a challenging task to observe these highly variable atmospheric constituents on a global scale with a temporal and spatial resolution sufficient for numerical weather prediction (NWP) and hydrological purposes. This study investigates the potential of the millimeter- and submillimeter-wavelength range on space-borne sensors for hydrometeor and surface precipitation rate observations. The approach is based on simulations with cloud resolving models (CRMs) coupled to a radiative transfer (RT) model. The simulations are performed for mid-latitude cases covering a broad band of precipitation events such as heavy convective and light stratiform winter precipitation. Realistic atmospheric conditions were simulated with two mesoscale CRMs: the Meso-scale NonHydrostatic model (Meso-NH) on a 10 km and the COSMO-DE (COnsortium for Small-scale MOdeling-DEutschland) on a 2.8 km horizontal resolution. When calculating brightness temperatures for satellite observations with the one-dimensional radiative transfer model MWMOD (MicroWave MODel), the detailed cloud microphysics and the three-dimensional fields of temperature, humidity, and pressure of the CRMs are considered in the calculation of the interaction parameters. The model framework has been evaluated by comparing the simulated brightness temperature fields to observations of the Special Sensor Microwave Imager (SSM/I) as well as to those of the Advanced Microwave Sounding Unit-B (AMSU-B). The results show a good agreement as long as the CRMs capture the atmospheric situation correctly. Consequently, by coupling the radiative transfer model for microwave radiation to CRMs it is possible to evaluate these models through comparison to microwave satellite observations. Brightness temperatures for frequencies between 50 and 428 GHz at nine observation angles have been simulated for five mid-latitude cases at two time steps. In combination with the vertically integrated hydrometeor contents, these brightness temperature simulations have been used to set up a database. On the basis of this database simple retrieval algorithms have been developed to estimate the potential of the millimeter- and submillimeter-wavelength region for precipitation and hydrometeor observations. The results show, that especially for snow and graupel, the total column content can be retrieved accurately with relative errors smaller than 20% in stratiform precipitation cases over land and ocean surfaces. The performance for rain water path is similar to the one for graupel and snow in light precipitation cases. For the cases with higher precipitation amounts, the relative errors for rain water path are larger especially over land. The same behavior can be seen in the surface rain rate retrieval with the difference that the relative errors are doubled in comparison to the rain water path. Algorithms with a reduced number of frequencies show that window channels at higher frequencies are important for the surface rain rate retrieval. These are sensitive to the scattering in the ice phase related to the rain below. For the frozen hydrometeor retrieval, good results can be achieved by retrieval algorithms based only on frequencies at 150 GHz and above which are suitable for geostationary applications due to their reduced demands concerning the antenna size

    Passive millimeter-wave retrieval of global precipitation utilizing satellites and a numerical weather prediction model

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    Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, February 2007.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 229-234).This thesis develops and validates the MM5/TBSCAT/F([lambda]) model, composed of a mesoscale numerical weather prediction (NWP) model (MM5), a two-stream radiative transfer model (TBSCAT), and electromagnetic models for icy hydrometeors (F([lambda])), to be used as a global precipitation ground-truth for evaluating alternative millimeter-wave satellite designs and for developing methods for millimeter-wave precipitation retrieval and assimilation. The model's predicted millimeter-wave atmospheric radiances were found to statistically agree with those observed by satellite instruments [Advanced Microwave Sounding Unit-A/B (AMSU-A/B)] on the United States National Ocean and Atmospheric Administration NOAA-15, -16, and -17 satellites over 122 global representative storms. Whereas such radiance agreement was found to be sensitive to assumptions in MM5 and the radiative transfer model, precipitation retrieval accuracies predicted using the MM5/TBSCAT/F([lambda]) model were found to be robust to the assumptions.(cont.) Appropriate specifications for geostationary microwave sounders and their precipitation retrieval accuracies were studied. It was found that a 1.2-m micro-scanned filled-aperture antenna operating at 118/166/183/380/425 GHz, which is relatively inexpensive, simple to build, technologically mature, and readily installed on a geostationary satellite, could provide useful observation of important global precipitation with ~20-km resolution every 15 minutes. AMSU global precipitation retrieval algorithms for retrieving surface precipitation rate, peak vertical wind, and water-paths for rainwater, snow, graupel, cloud water, cloud ice, and the sum of rainwater, snow, and graupel, over non-icy surfaces were developed separately using a statistical ensemble of global precipitation predicted by the MM5/TBSCAT/F([lambda]) model. Different algorithms were used for land and sea, where principal component analysis was used to attenuate unwanted noises, such as surface effects and angle dependence. The algorithms were found to perform reasonably well for all types of precipitation as evaluated against MM5 ground-truth. The algorithms also work over land with snow and sea ice, but with a strong risk of false detections. AMSU surface precipitation rates retrieved using the algorithm developed in this thesis reasonably agree with those retrieved for the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) aboard the Aqua satellite over both land and sea.(cont.) Surface precipitation rates retrieved using the Advanced Microwave Sounding Unit (AMSU) aboard NOAA-15 and -16 satellites were further compared with four similar products derived from other systems that also observed the United States Great Plains (USGP) during the summer of 2004. These systems include AMSR-E aboard the Aqua satellite, the Special Sensor Microwave/Imager (SSM/I) aboard the Defense Meteorological Satellite Program (DMSP) F-13, -14, and -15 satellites, the passive Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) aboard the TRMM satellite, and a surface precipitation rate product (NOWRAD), produced and marketed by Weather Services International Corporation (WSI) using observations from the Weather Surveillance Radar-1988 Doppler (WSR-88D) systems of the Next-Generation Weather Radar (NEXRAD) program. The results show the reasonable agreement among these surface precipitation rate products where the difference is mostly in the retrieval resolution, which depends on instruments' characteristics. A technique for assimilating precipitation information from observed millimeter-wave radiances to MM5 model was proposed. Preliminary study shows that wind and other correction techniques could help align observations at different times so that information from observed radiances is used at appropriate locations.by Chinnawat Surussavadee.Ph.D

    Cloud-radiation interactions and their parameterization in climate models

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    This report contains papers from the International Workshop on Cloud-Radiation Interactions and Their Parameterization in Climate Models met on 18-20 October 1993 in Camp Springs, Maryland, USA. It was organized by the Joint Working Group on Clouds and Radiation of the International Association of Meteorology and Atmospheric Sciences. Recommendations were grouped into three broad areas: (1) general circulation models (GCMs), (2) satellite studies, and (3) process studies. Each of the panels developed recommendations on the themes of the workshop. Explicitly or implicitly, each panel independently recommended observations of basic cloud microphysical properties (water content, phase, size) on the scales resolved by GCMs. Such observations are necessary to validate cloud parameterizations in GCMs, to use satellite data to infer radiative forcing in the atmosphere and at the earth's surface, and to refine the process models which are used to develop advanced cloud parameterizations
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